Due to fixed convolution kernel structure, it is intractable for Convolutional Neural Networks (CNN) to adequately deal with intact instance geometric transformations, which may paralyze the CNN’s paradigmatic feature extraction. Inspired by Deformable Convolutional Networks, in this work, we introduce stochastic deformatble kernels, where kernel shape is assumed to be random vaiable. Stochastic deformable kernels may extract non-local alienated structural features, which slightly condones geometric transformations. Variational learning of the model based on two approximate posteriors is derived. And experiments over transformed MNIST dataset demonstrate the validity of this approach.